In electricity distribution systems, loss occurs when current flows through the conductors in the system. This energy loss through a conductor may be calculated according to I2R, where I is the current through conductor whose resistance is R. The net demand or current flow depends on the voltage profile on the feeders. Reactive compensation can reduce unnecessary current flows and in turn reduce losses. Voltage regulation affects the effective loading of feeders, as well as the energy losses.
Voltage and Var optimization (VVO) systems are employed in electricity distribution systems to optimize the distribution of voltages and currents on distribution systems. VVO systems endeavor to maximize efficiency of energy delivery by controlling the tap changer settings of voltage regulating transformers (Voltage) and reactive power resources (capacitor banks) (Var) by employing online system models and demand forecasts.
With reference to FIG. 1, an electricity distribution network is shown. As can be seen, a substation provides power to a plurality of loads through the substation transformers, feeders, and laterals. Distributed at various points in the distribution network are capacitor banks C that may be fixed or switched, and voltage regulators that can be locally or remotely controlled to alter the tap settings. The connectivity of the network and the status of the various equipment, such as transformers, loads, capacitors, voltage regulators, are monitored via sensors and a communication infrastructure. Monitored data may include voltage, current and/or power at or through various points or conductors. This information is transmitted to a distribution management system (DMS) or a substation automation system (SAS). Upon receiving the updated status information, the system model (load flow model) within the DMS is updated. A load forecast is performed based on the SCADA data, customer billing data, and/or data collected from advanced metering infrastructure (AMI). The VVO, based on the load forecasts, the system model, and the available control information, then determines the best tap settings for the voltage regulators and on load tap change (OLTC) transformers, and the Var resources such as switched shunt capacitors or reactors. Control commands are then transmitted back to the various elements in the distribution grid where the control actions are carried out, bring the system to a more efficient operating state.
VVO is the decision making process that analyzes the input data from the field and generates the control signals to be transmitted to the controllers in the filed. Var optimization (VARO) is a subsystem of a VVO system that processes the capacitor switching optimization problem. The VARO is a self contained process that may work stand alone or in conjunction with a Voltage Regulation Optimization (VRO) system to provide integrated VVO solutions. The present disclosure is directed toward a design for VAR optimization.
The concept of optimizing energy delivery efficiency on electric distribution systems by means of capacitor bank switching dates back several decades and many in the industry and the research communities have attempted to develop effective solution methodology and process. Most solution approaches proposed to date are applicable to small, very simplified academic models, and are not suitable for large scale, meshed, multi-source, multi-phase unbalanced distribution systems that are common in real-world distribution networks. The deficiencies in conventional methods are due to (1) the model being too simplified to represent a real system, by assuming radial topology, balanced construction and operation, or ignoring the effect of transformer connections (for example, wye to delta connections), (2) the computation efficiency being so low that the solution can not be scaled for either online or offline applications for large system, or (3) the methods are not general enough and have limited optimizing capability
Thus, there is a need in the art for an optimization solution applicable to large scale, meshed, multi-source, multi-phase unbalanced distribution systems.